Flower border long-term effectiveness evaluation method in combination with multi-dimensional indexes

Abstract

The invention provides a flower border long-term evaluation method in combination with a multi-dimensional index, and relates to the technical field of gardening and landscape design, and the method comprises the steps: carrying out the low-maintenance management of a target flower border based on an evaluation period, and obtaining a maintenance record log; performing long-term judgment analysis on the flower border, performing multi-dimensional analysis in combination with an evaluation period, then setting a plurality of flower border long-term indexes, performing multi-dimensional evaluation on the target flower border to generate a multi-dimensional evaluation data set, and performing comprehensive evaluation calculation on the long-term to obtain a long-term comprehensive evaluation index for performing intelligent evaluation on the long-term. The problems that flower border long-term evaluation is lack of systematic and quantitative flower border evaluation standards and long-term ecological and aesthetic benefits are difficult to comprehensively and objectively measure are solved, and the effects of accurately evaluating flower border long-term effectiveness, providing scientific basis for optimization, maintenance and management and improving the continuity of ecological and ornamental values are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of gardening and landscape design, and in particular to a method for evaluating the long-term effectiveness of a flower border by combining multi-dimensional indicators. Background Art

[0002] Flower borders are an element of horticultural design, usually in parks, gardens, urban spaces or private courtyards, where a variety of wild flowers are interlaced in the natural state of the edge of the woodland or an area where a variety of flowers and plants are planted in an artistic form. The design of flower borders can be simple or complex, with the aim of creating a beautiful, colorful visual focal point. These designs can be changed according to seasonal changes to maintain year-round beauty. In urban environments, flower borders not only enhance the beauty of the city and the quality of life of residents, but also provide habitats and ecosystem services for biodiversity. Compared with the classic concept of flower borders, long-term flower borders emphasize the stability of plant communities, the longevity of plant varieties and the long-term ornamental effect. However, although the construction and design of flower borders have made certain progress, existing technologies still have many limitations in evaluating the long-term effectiveness of flower borders. Current evaluation methods mostly rely on empirical judgment or simple visual inspection, lacking a systematic and quantitative evaluation framework. In addition, the maintenance and management of flower borders also face many challenges, such as how to maintain the long-term healthy growth of plants, how to maintain the original design theme and aesthetic effect of the flower border, etc.

[0003] In summary, the existing long-term effectiveness evaluation of flower borders lacks systematic and quantitative flower border evaluation standards, making it difficult to comprehensively and objectively measure their long-term ecological and aesthetic benefits. Summary of the invention

[0004] The present application provides a method for evaluating the long-term effectiveness of flower borders by combining multi-dimensional indicators, which is used to solve the technical problem that the existing long-term effectiveness evaluation of flower borders lacks systematic and quantitative flower border evaluation standards, making it difficult to comprehensively and objectively measure their long-term ecological and aesthetic benefits.

[0005] The present application provides a method for evaluating the long-term effectiveness of a flower border by combining multi-dimensional indicators, the method comprising:

[0006] Extract a target flower border, perform low-maintenance management on the target flower border based on an evaluation cycle, and obtain a flower border maintenance record log; perform long-term determination and analysis on the target flower border according to the flower border maintenance record log to generate a determination result; perform multi-dimensional analysis on the target flower border based on the determination result and the evaluation cycle, and set multiple flower border long-term effectiveness indicators according to the analysis result; perform multi-dimensional evaluation on the target flower border according to the multiple flower border long-term effectiveness indicators to generate a multi-dimensional evaluation data set; perform a comprehensive evaluation calculation on the long-term effectiveness of the target flower border according to the multi-dimensional evaluation data set to obtain a long-term effectiveness comprehensive evaluation index of the target flower border, and perform an intelligent long-term effectiveness evaluation on the target flower border according to the long-term effectiveness comprehensive evaluation index.

[0007] One or more technical solutions provided in the application have at least the following technical effects or advantages:

[0008] The method for evaluating the long-term effectiveness of flower borders in combination with multi-dimensional indicators provided in the present application extracts a target flower border, performs low-maintenance management on the target flower border based on an evaluation period, and obtains a flower border maintenance record log; performs long-term determination and analysis on the target flower border according to the flower border maintenance record log to generate a determination result; performs multi-dimensional analysis on the target flower border based on the determination result and the evaluation period, and sets a plurality of flower border long-term effectiveness indicators according to the analysis result; performs multi-dimensional evaluation on the target flower border according to the plurality of flower border long-term effectiveness indicators to generate a multi-dimensional evaluation data set; performs a comprehensive evaluation calculation on the long-term effectiveness of the target flower border according to the multi-dimensional evaluation data set to obtain a long-term comprehensive evaluation index of the target flower border, and performs an intelligent long-term evaluation on the target flower border according to the long-term comprehensive evaluation index, thereby solving the technical problem that the existing long-term effectiveness evaluation of flower borders lacks a systematic and quantitative flower border evaluation standard and is difficult to comprehensively and objectively measure their long-term ecological and aesthetic benefits, thereby achieving an accurate evaluation of the long-term effectiveness of flower borders, providing a scientific basis for optimizing maintenance and management, and improving the technical effect of the sustainability of their ecological and ornamental values. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 A flow chart of a method for evaluating the long-term effectiveness of a flower border combining multi-dimensional indicators is provided for this application.

[0010] Figure 2 A flow chart of obtaining a comprehensive evaluation index for long-term effectiveness in a long-term effectiveness evaluation method for a flower border combining multi-dimensional indicators is provided for this application. DETAILED DESCRIPTION

[0011] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0012] Examples, such as Figure 1 As shown, the present application provides a method for evaluating the long-term effectiveness of a flower border by combining multi-dimensional indicators, the method comprising:

[0013] Extract the target flower border, perform low-maintenance management on the target flower border based on the evaluation cycle, and obtain the flower border maintenance record log.

[0014] Specifically, it is necessary to first clarify the scope of the target flower border, that is, to extract the target flower border. The target flower border refers to a flower border unit constructed in a specific area and requiring long-term evaluation, which has relatively clear boundaries and independent design themes. The extraction of the target flower border can be determined by on-site mapping or reference to the construction design drawing to ensure the clarity of its boundaries and the consistency of the evaluation scope. Subsequently, the target flower border is managed with low maintenance according to the set evaluation cycle. Low maintenance management refers to basic maintenance of the flower border with minimal human intervention and resource investment within a specific time period. Its specific operations include: only necessary pruning or replacement when the plant withers or dies; regular watering and fertilization according to the growth needs of the plant, but no additional use of pesticides; for pests and diseases, only take prevention and control measures when necessary; during the entire evaluation cycle, no hard materials (such as paving or small pieces) are replaced or modified. The core of low maintenance management is to maintain the basic ecology and ornamental functions of the flower border, while minimizing human intervention as much as possible, so as to objectively reflect the long-term performance of the flower border.

[0015] In the process of low-maintenance management, the maintenance log of the flower border is recorded comprehensively and accurately. The flower border maintenance log is an important data carrier that reflects the dynamic changes of the flower border. It contains multi-dimensional information, including but not limited to the type, number of plants, coverage area, watering frequency, fertilization amount, pruning or replacement records, etc. At the same time, the maintenance status of hard materials, such as the integrity of small pieces and the loss of paving, must be recorded in detail. Each maintenance operation must be recorded item by item and marked with a specific date to ensure the integrity and traceability of the data. For example, in a maintenance activity, the log may contain the following content: the date is July 15, 2024, the watering volume is 100 liters, and the coverage area is 30 square meters of plant area; 15 withered plants were pruned and 10 dead plants were replaced (the species after replacement is Aster, and the area is 5 square meters). Through such detailed logs, accurate basic data can be provided for subsequent evaluation and data analysis to ensure the scientificity and repeatability of long-term evaluation.

[0016] In summary, by extracting target flower borders, setting evaluation cycles for low-maintenance management, and obtaining maintenance record logs, reliable preliminary data support and an objective evaluation basis were provided for the multi-dimensional long-term evaluation method.

[0017] A long-term determination analysis is performed on the target flower border according to the flower border maintenance record log to generate a determination result.

[0018] Optionally, by extracting and organizing the data recorded in the maintenance log, the dynamic changes of the target flower border during the evaluation period can be fully understood. When conducting long-term determination analysis, it is necessary to extract multiple core dimensional data of the target flower border from the maintenance log for analysis. The results obtained include but are not limited to the survival rate of the original plants, the change ratio of plant coverage area, the change of plant levels and proportional relationships, the performance level of theme presentation, and the maintenance status of hard materials. Taking the survival rate as an example, the calculation formula is: the number of surviving plants × 100% / the number of plants initially planted. For example, if there are 100 plants initially planted in a flower border and 80 plants survived at the end of the evaluation period, the survival rate is 80%. Similarly, the change ratio of coverage area can be calculated by comparing the current plant coverage area with the initial coverage area. In the determination analysis process, the above multi-dimensional data is systematically compared and analyzed with the data at the initial stage of flower border construction to evaluate the long-term performance of the flower border during the evaluation period. To ensure the accuracy and scientificity of the analysis, data modeling or statistical methods can be used for quantitative calculations, such as introducing the membership function method to normalize the data of each dimension and convert the data of different dimensions into a unified standardized value, so as to facilitate cross-dimensional comprehensive analysis. Taking the changes in plant levels and proportional relationships as an example, more than 15 horticultural experts can be invited to score the plant distribution and sense of hierarchy at the end of the evaluation cycle. The scoring range is based on a percentage system, increasing step by step from "very poor (0%-20%)" to "very good (80%-100%)", and the average of the scores of multiple experts is used as the comprehensive scoring result of this dimension. For example, the score of the plant hierarchy of a flower border is 85%, indicating that it performs well. Finally, the long-term determination result of the target flower border is generated based on the analysis results of each dimension. The judgment result can be presented in the form of a quantitative score. For example, by calculating the comprehensive score of each dimension and taking the average value to generate the overall score of the flower border, if the score is higher than the set long-term effectiveness standard threshold (such as 0.70), it is judged to have long-term effectiveness; if the score is lower than the standard, it is judged to not have long-term effectiveness.

[0019] Through the above steps, the long-term effectiveness of the target flower border can be systematically judged and analyzed in a scientific and objective manner, thereby providing a data basis and judgment basis for subsequent optimization management and multi-dimensional analysis.

[0020] Based on the determination result and the evaluation period, a multi-dimensional analysis is performed on the target flower border, and a plurality of flower border long-term effectiveness indicators are set according to the analysis result.

[0021] Furthermore, based on the generated long-term determination results and the evaluation cycle of the target flower border, the basic data set for multi-dimensional analysis is determined. The evaluation cycle refers to a pre-set time interval, such as one or three years, which is used to record and analyze the long-term changes in the flower border. The long-term determination results provide preliminary quantitative data on the survival rate, coverage area, hierarchical ratio, theme presentation and hard material maintenance status of the target flower border, which will be used as input parameters for multi-dimensional analysis. Next, the target flower border is hierarchically analyzed and divided into five analysis dimensions: plant survival analysis layer, plant area analysis layer, plant hierarchy analysis layer, theme analysis layer and material maintenance analysis layer. Each analysis dimension has a specific analysis goal and operation process. For example, the plant survival analysis layer focuses on the survival status of plants, which is to evaluate the survival rate by comparing the number of surviving plants with the initial planting number; the plant area analysis layer focuses on the dynamic changes in the coverage area, and calculates the change ratio by comparing the current coverage area with the initial coverage area. In the process of hierarchical analysis, the actual data of the target flower border needs to be extracted from the maintenance record log. For example, assuming that the target flower border has an initial plant coverage area of ​​50 square meters and a current coverage area of ​​40 square meters during the evaluation period, the plant coverage area change ratio is 40 / 50=80%. Similarly, in the plant hierarchy analysis layer, the plant hierarchy can be quantitatively analyzed through expert scoring. For example, the current score is 85 points, while the initial score is 90 points, indicating that the hierarchy has slightly decreased. Subsequently, based on the analysis results of each analysis layer and combined with the time nodes within the evaluation period, multiple flower border long-term indicators are set. These indicators include the original plant survival rate indicator, the original plant area ratio indicator, the plant hierarchy ratio indicator, the theme presentation index, and the hard material maintenance status indicator. The setting of each indicator needs to be based on the analysis data to ensure that it can accurately reflect the long-term performance of the target flower border. For example, if the plant survival rate is higher than 80%, the area ratio exceeds 70%, and the hierarchy ratio and theme presentation scores are both higher than 60%, it can be preliminarily judged that the flower border has good long-term performance.

[0022] Through multi-dimensional analysis and indicator setting, the long-term characteristics of the target flower border can be comprehensively quantified, providing scientific data support for subsequent multi-dimensional evaluation and comprehensive evaluation, and ensuring the objectivity and accuracy of the evaluation results.

[0023] A multi-dimensional evaluation is performed on the target flower border according to the multiple flower border long-term effectiveness indicators to generate a multi-dimensional evaluation data set.

[0024] Exemplarily, the target flower border is evaluated item by item according to the set multiple long-term indicators of the flower border, including the survival rate of the original plants, the proportion of the original plant area, the proportion of the plant level, the degree of theme presentation and the maintenance of hard materials. The evaluation of each indicator is based on specific analytical data and calculation methods. For example, for the original plant survival rate indicator, it is necessary to extract the actual number of surviving plants from the maintenance record log and compare it with the initial planting number; for the original plant area proportion indicator, the current plant coverage area of ​​the target flower border is compared with the initial plant coverage area. This evaluation reflects the ability to maintain the plant growth area in the flower border. For the plant level proportion indicator, the target flower border plant level and proportion relationship are quantitatively analyzed by the expert scoring method. The scoring standard adopts a percentage system and is specifically divided into five levels, namely 0%-20%, 20% (not included)-40%, 40% (not included)-60%, 60% (not included)-80%, 80% (not included)-100%, respectively representing very poor, poor, general, good and good performance of the flower border. In the evaluation of the theme presentation index, experts are invited to score according to the theme expression of the current flower border. Assuming that the initial theme score of a flower border is 90 points and the current score is 88 points, it means that the theme presentation effect remains basically stable. The hard material maintenance condition index is quantified by checking the integrity and loss of hard materials such as structures and pavements in the target flower border. The five-level percentage system is also used. For example, a score of 95% indicates that the hard materials are in good condition. After completing each individual evaluation, the evaluation results of the above indicators are summarized to generate a multidimensional evaluation data set. A multidimensional evaluation data set is a collection of all indicator evaluation data, which can systematically reflect the overall performance of the target flower border. For example, the multidimensional data set includes complete data items with a survival rate of 85%, an area share of 80%, a hierarchical proportion score of 85%, a theme presentation score of 88%, and a hard material maintenance score of 95%.

[0025] The generated multidimensional evaluation data set can comprehensively display the multidimensional performance of the target flower border, provide scientific and quantitative data support for the subsequent long-term comprehensive evaluation, and provide an objective basis for optimizing management and design, ensuring the accuracy and completeness of the target flower border evaluation, thereby achieving a comprehensive analysis of the long-term performance of the flower border.

[0026] A comprehensive evaluation calculation of the long-term effectiveness of the target flower border is performed according to the multidimensional evaluation data set to obtain a long-term comprehensive evaluation index of the target flower border, and an intelligent long-term effectiveness evaluation of the target flower border is performed according to the long-term comprehensive evaluation index.

[0027] Specifically, various evaluation data are collected and integrated from the multidimensional evaluation data set, including scores of key indicators such as plant survival rate, area proportion, plant layer proportion, theme presentation and hard material maintenance status. These data will be integrated by weighted summation, where the weight of each indicator can be set in advance according to the specific needs of flower border design and maintenance. For example, if the visual performance and ecological function of the target flower border are equally important, higher weights may be given to theme presentation and plant survival rate. When performing comprehensive evaluation calculations, the weighted score of each indicator will be calculated using a formula, and these scores will be accumulated to generate a comprehensive evaluation index. After the calculation of the comprehensive evaluation index is completed, the target flower border is intelligently evaluated for long-term effectiveness based on the obtained index. Intelligent evaluation refers to the use of the calculated comprehensive evaluation index to automatically determine the long-term effectiveness level of the flower border through a preset evaluation standard or threshold. For example, a threshold can be set, such as a comprehensive evaluation index greater than 0.8 indicates excellent long-term effectiveness, 0.6 to 0.8 indicates good, and less than 0.6 requires improvement. The evaluation results can be used directly to guide future flower border management and improvement strategies, such as taking targeted improvement measures for poorly performing indicators, or optimizing flower border design to improve its overall long-term performance. Through this method, the design, construction and maintenance of flower borders can be more scientific and precise, ensuring their long-term sustainable development and maximizing their visual and ecological benefits. This not only improves the accuracy and practicality of flower border evaluation, but also makes flower border management more efficient and scientific through intelligent analysis and judgment, which is of great significance to urban greening management, ecological city construction and the continuous optimization of garden landscapes.

[0028] Furthermore, based on the determination result and the evaluation period, a multi-dimensional analysis is performed on the target flower border, and a plurality of flower border long-term effectiveness indicators are set according to the analysis result. The method includes:

[0029] According to the evaluation cycle, the determination result is used as index data, and the flower border maintenance record log is traversed for hierarchical analysis to determine multiple dimensional layers, which include a plant survival analysis layer, a plant area analysis layer, a plant hierarchy analysis layer, a plant theme analysis layer, and a material maintenance analysis layer.

[0030] Based on the evaluation cycle, real-time data extraction is performed on the target flower border according to the multiple dimensional layers to obtain multiple actual data of the target flower border.

[0031] The multiple actual data are synchronized to the multiple dimensional layers in sequence, data analysis and comparison are performed on the multiple actual data through the multiple dimensional layers, and the multiple flower border long-term effectiveness indicators are set.

[0032] Optionally, based on the evaluation cycle, i.e., a predetermined time period, the determination result is used as index data. This step involves using previous long-term determination results, such as the survival rate and growth status of plants, as indexes or reference points to navigate and interpret the maintenance log. The determination result thus becomes a bridge between actual maintenance activities and theoretical analysis models, ensuring that the analysis conducted is closely related to the actual flower border conditions. Next, the flower border maintenance log is traversed for hierarchical analysis to determine multiple dimensional layers, which include: plant survival analysis layer, plant area analysis layer, plant hierarchy analysis layer, plant theme analysis layer, and material maintenance analysis layer. Each layer analyzes a specific aspect of the flower border, for example, the plant survival analysis layer focuses on the survival rate and cause of death of plants; the plant area analysis layer evaluates the changes in plant coverage; the plant hierarchy analysis layer explores the hierarchical relationship and visual effect between plants; the plant theme analysis layer evaluates how the plant layout expresses the design theme of the flower border; and the material maintenance analysis layer monitors the maintenance and durability of hard materials such as garden paths and stone walls. Then, based on the evaluation cycle, real-time data extraction is performed on the target flower border according to the multiple dimensional layers. This step involves extracting actual data related to each analysis layer from the maintenance records, such as the real-time survival number of plants, the current coverage area, etc. The extraction of real-time data enables the analysis to reflect the latest status of the flower border, providing the necessary data support for accurate evaluation. Finally, the multiple actual data are synchronized to the multiple dimensional layers in turn, and the multiple actual data are analyzed and compared through the multiple dimensional layers. In this process, each dimensional layer uses specialized analysis techniques and algorithms, such as statistical analysis, comparative research or pattern recognition, to analyze and interpret the data. Based on these comparison and analysis results, the final multiple long-term indicators of flower borders are set, such as plant survival rate indicators, area proportion indicators, etc. These indicators can comprehensively and accurately reflect the long-term performance of flower borders.

[0033] Through the above steps, not only a systematic and scientific method is provided to evaluate and analyze the long-term effectiveness of flower borders, but also, through precise data processing and intelligent analysis, flower border managers can get specific guidance on flower border maintenance and improvement. The implementation of this method will greatly improve the aesthetics of flower borders and the durability of their ecological functions, and ensure the efficiency and scientificity of flower border design and maintenance.

[0034] Furthermore, the multiple actual data are synchronized to the multiple dimensional layers in sequence, data parsing and comparison are performed on the multiple actual data through the multiple dimensional layers, and the multiple flower border long-term effectiveness indicators are set. The method includes:

[0035] The multiple actual data are analyzed and classified based on the multiple dimensional layers to obtain the actual number of surviving plants, actual plant coverage area parameters, actual plant level distribution parameters, actual theme presentation effect parameters, and actual material maintenance position parameters.

[0036] The plant survival analysis layer compares and calculates the initial number of plants with the number of surviving plants to set the original plant survival rate index.

[0037] The initial plant coverage area parameter and the actual plant coverage area parameter are compared and analyzed through the plant area analysis layer to set the original plant area ratio index.

[0038] The initial plant level distribution parameters and the actual plant level distribution parameters are compared and analyzed through the plant level analysis layer to set a plant level ratio index.

[0039] The plant theme analysis layer compares and analyzes the initial flower border theme and the actual theme presentation effect parameters to set a theme presentation index.

[0040] The material maintenance analysis layer compares and analyzes the initial hard material position and the actual material maintenance position parameters to set the hard material position index.

[0041] The original plant survival rate index, the original plant area ratio index, the plant level ratio index, the theme presentation index, and the hard material position index are correlated and integrated to set the multiple flower border long-term indicators.

[0042] Furthermore, the collected actual data are parsed and classified based on the set multiple dimensional layers, including plant survival analysis layer, plant area analysis layer, plant hierarchy analysis layer, plant theme analysis layer and material maintenance analysis layer. Each layer analyzes the specific attributes of the flower border. Actual data, such as the number of surviving plants, coverage area, hierarchy distribution, theme presentation effect and material maintenance, are extracted from the flower border maintenance records and classified according to these dimensions. Subsequently, the initial number of plants and the actual number of surviving plants are compared and calculated through the plant survival analysis layer. For example, if 100 plants are planted at the beginning and 80 plants survive at the evaluation time, the original plant survival rate index is 80%, which directly reflects the maintenance capacity and ecological health status of the flower border. Next, the plant area analysis layer is responsible for comparing the initial plant coverage area with the actual plant coverage area. Assuming that the initial coverage area is 500 square meters and the current coverage area is reduced to 450 square meters, the original plant area ratio index will be calculated as 450 / 500, that is, 90%, which reveals the continuity of the flower border in terms of spatial distribution and growth development. In the plant level analysis layer, the initial plant level distribution parameters are compared with the actual plant level distribution parameters, which may involve visual assessment or quantitative measurement of changes in plant height and distribution, and then the plant level ratio index is set, which helps to evaluate the visual and structural integrity of the flower border. The plant theme analysis layer sets the theme presentation index by comparing the initial theme setting with the actual theme presentation effect. For example, if the original theme aims to show the wildness of nature, and the actual presentation effect score drops from 100 to 85, it indicates that the degree of theme realization has declined. Finally, the material maintenance analysis layer evaluates the initial position and actual maintenance status of garden paths, decorations and other hard materials. For example, if the original stone road is partially damaged due to weather and other factors, the hard material position index can indicate the need for repair or improvement. Based on the above indicators, the original plant survival rate index, the original plant area ratio index, the plant level ratio index, the theme presentation index, and the hard material position index are correlated and integrated, and finally multiple long-term indicators of the flower border are set. These indicators together constitute the basis for the comprehensive evaluation of the long-term effectiveness of the flower border, making the evaluation results not only comprehensive but also detailed, providing a scientific basis for future flower border management and optimization.

[0043] Furthermore, a multi-dimensional evaluation is performed on the target flower border according to the plurality of flower border long-term effectiveness indicators to generate a multi-dimensional evaluation data set, and the method includes:

[0044] The target flower border is subjected to plant survival assessment according to the original plant survival rate index to obtain survival assessment data.

[0045] The growth area of ​​the target flower border is evaluated according to the original plant area ratio index to obtain area coverage evaluation data.

[0046] The target flower border is evaluated for construction level according to the plant level ratio index to obtain level ratio evaluation data.

[0047] The presentation degree of the target flower border is evaluated according to the theme presentation degree index to obtain presentation effect evaluation data.

[0048] A material evaluation is performed on the target flower border according to the hard material position index to obtain material maintenance evaluation data.

[0049] The survival assessment data, the area coverage assessment data, the level ratio assessment data, the presentation effect assessment data, and the material maintenance assessment data are integrated through the multiple dimensional layers to obtain the multidimensional assessment data set.

[0050] Exemplarily, the target flower border is evaluated for plant survival according to the set original plant survival rate index. This step involves counting the number of surviving plants at the end of the evaluation period and comparing it with the initial number of plants to calculate the survival rate. For example, if 100 plants are planted initially, 80 plants survive at the end of the evaluation period, and the survival rate is 80%. This data will be recorded as survival evaluation data. Next, the growth area of ​​the target flower border is evaluated according to the original plant area ratio index, which involves measuring the actual coverage area of ​​the current plants and comparing it with the initial coverage area. Assuming that the initial coverage area is 500 square meters and the current coverage area is 450 square meters, the area ratio is 90%, and this data will be recorded as area coverage evaluation data. Next, the construction level of the target flower border is evaluated according to the plant level ratio index. This evaluation may include observing and recording the distribution and ratio of high and low levels of plants to reflect the hierarchy of the flower border design. If the evaluation result shows that the level distribution score is 85%, this data is recorded as level ratio evaluation data. In addition, the theme presentation degree of the target flower border is evaluated according to the theme presentation degree index, by evaluating whether the flower border can effectively convey its design theme, such as natural wildness or rich colors, and giving a corresponding score. For example, if the theme presentation effect is 90%, this data is recorded as presentation effect evaluation data. Finally, according to the hard material position index, the maintenance status of hard materials such as garden paths and stone walls in the target flower border is evaluated to check whether these materials are well maintained without obvious damage or dislocation. If the evaluation score is 95%, this data is recorded as material maintenance evaluation data. All these evaluation data extracted from different dimensional layers: survival evaluation data, area coverage evaluation data, layer ratio evaluation data, presentation effect evaluation data, and material maintenance evaluation data are then integrated together through a comprehensive method to form a multidimensional evaluation data set. This data set provides a comprehensive view of the long-term effectiveness of the flower border, reflects the comprehensive performance of multiple key performance indicators, and provides a basis for further decision-making and improvement. This systematic evaluation method not only enhances the reliability of the evaluation, but also improves the pertinence and effectiveness of management and maintenance strategies.

[0051] Furthermore, the survival assessment data, the area coverage assessment data, the level ratio assessment data, the presentation effect assessment data, and the material maintenance assessment data are integrated through the multiple dimensional layers to obtain the multidimensional assessment data set, and the method includes:

[0052] The target flower border is calculated according to the survival evaluation data through the plant survival analysis layer to generate a plant adaptation coefficient.

[0053] The target flower border is calculated according to the area coverage assessment data through the plant area analysis layer to generate a plant growth coefficient.

[0054] The target flower border is calculated according to the hierarchical proportion evaluation data through the plant hierarchical analysis layer to generate a plant construction proportion coefficient.

[0055] The target flower border is calculated according to the presentation effect evaluation data through the plant theme analysis layer to generate a theme consistency coefficient.

[0056] The material maintenance analysis layer calculates the target flower border according to the material maintenance evaluation data to generate a hard material usage coefficient.

[0057] The survival assessment data, the area coverage assessment data, the level proportion assessment data, the presentation effect assessment data and the material maintenance assessment data are sequentially subjected to multidimensional clustering integration according to the plant adaptation coefficient, the plant growth coefficient, the plant construction proportion coefficient, the theme consistency coefficient and the hard material usage coefficient to determine the multidimensional assessment data set.

[0058] Specifically, the survival assessment data is processed by the plant survival analysis layer. In this layer, the plant adaptation coefficient is calculated by comparing the actual number of surviving plants recorded with the initial number of plants. The plant adaptation coefficient is an indicator of the ability of plants in the flower border to adapt to their growth environment, reflecting the plant's ability to respond to environmental changes. For example, if 100 plants are initially planted and 80 survive, the plant adaptation coefficient may be calculated as 0.8. Next, the plant area analysis layer processes the area coverage assessment data and calculates the plant growth coefficient, which indicates the plant's ability to grow and expand based on the comparison of the actual coverage area of ​​the plant with the initial area. If the initial area is 500 square meters and the current area is 450 square meters, the plant growth coefficient is 0.9, indicating that the plant coverage area has decreased slightly. Then, the plant hierarchy analysis layer uses the hierarchy proportion assessment data to calculate the plant construction proportion coefficient. This coefficient evaluates whether the layout of plants in the flower border grows well according to the design hierarchy, such as whether the hierarchy structure remains clear. The score is based on the expert's visual evaluation. For example, a hierarchy proportion score of 85 points reflects that the plant hierarchy is well maintained in the overall design of the flower border. In addition, the plant theme analysis layer uses the presentation effect evaluation data to generate the theme consistency coefficient, which measures the consistency of the visual and theme performance of the flower border. If the original theme setting score is 100 points and the actual evaluation is 95 points, the theme consistency coefficient is 0.95, indicating that the flower border maintains the design theme well. Finally, the material maintenance analysis layer calculates the hard material use coefficient, which is based on the maintenance status of hard materials such as stone paths and gardening decorations. If the evaluation shows that 95% of the materials are still in good condition, the hard material use coefficient is 0.95. By applying the plant adaptation coefficient, plant growth coefficient, plant construction ratio coefficient, theme consistency coefficient, and hard material use coefficient to the corresponding evaluation data in turn, and performing multidimensional clustering integration, the multidimensional evaluation data set is finally determined. This data set integrates detailed information collected from various dimensions, provides comprehensive and in-depth data support for the long-term comprehensive evaluation of flower borders, ensures the accuracy and practicality of the evaluation, and makes the management and maintenance decisions of flower borders more scientific and effective.

[0059] Furthermore, before performing a comprehensive evaluation calculation of the long-term effectiveness of the target flower border according to the multi-dimensional evaluation data set, the method includes:

[0060] Based on the impact analysis of the survival assessment data, the area coverage assessment data, the level proportion assessment data, the presentation effect assessment data and the material maintenance assessment data according to the plant adaptation coefficient, the plant growth coefficient, the plant construction proportion coefficient, the theme consistency coefficient and the hard material usage coefficient, a plurality of indicator influencing factors are obtained.

[0061] According to the multiple indicator influencing factors, the original plant survival rate index, the original plant area ratio index, the plant level proportion index, the theme presentation index, and the hard material location index are weighted to obtain multiple weight coefficients.

[0062] Based on the multiple indicator influencing factors combined with the multiple weight coefficients, an impact association analysis is performed to construct a long-term impact association network of the target flower border.

[0063] A long-term comprehensive evaluation calculation is performed on the target flower border according to the long-term impact association network to obtain a long-term comprehensive evaluation index of the target flower border.

[0064] Furthermore, based on the plant adaptation coefficient, plant growth coefficient, plant construction ratio coefficient, theme consistency coefficient and hard material use coefficient, a detailed impact analysis is performed on the survival assessment data, area coverage assessment data, layer ratio assessment data, presentation effect assessment data and material maintenance assessment data. The analysis aims to identify how the assessment data corresponding to each coefficient affects the long-term performance of the flower border. For example, a high plant adaptation coefficient indicates a good plant survival rate, which directly affects the ecological stability of the flower border; a low plant growth coefficient may indicate a decrease in space utilization, affecting the visual perception of the flower border. Next, weights are assigned based on the multiple indicator influencing factors obtained from the impact analysis. Weight assignment involves quantifying the importance of each evaluation indicator and determining its relative importance in the comprehensive evaluation calculation. For example, if the visual effect and theme performance of the flower border are the main concerns of the evaluation, the theme consistency coefficient and the plant construction ratio coefficient may be assigned higher weights. Then, the impact association analysis is performed using the assigned weights and indicator influencing factors. This analysis combines all the evaluation data and coefficients to construct an analytical model called the long-term impact correlation network, which shows the interaction between different indicators and their overall impact on the long-term effectiveness of the flower border in a mutually correlated manner. Finally, a comprehensive evaluation of long-term effectiveness is calculated based on the long-term impact correlation network to obtain a comprehensive evaluation index of the long-term effectiveness of the target flower border. This calculation comprehensively considers each evaluation indicator and its corresponding weight, and calculates the final evaluation index through a mathematical model such as weighted average or other appropriate algorithms. For example, the score of each evaluation indicator is multiplied by its weight, and all the results are summed up to form a comprehensive evaluation index, which provides a quantitative measure of the overall long-term performance of the flower border.

[0065] Through the above steps, not only can the long-term effectiveness of flower borders be accurately evaluated, but also scientific data support can be provided to managers to help them optimize the design and maintenance strategies of flower borders, ensure the lasting beauty and ecological functions of flower borders, greatly improve the efficiency and effectiveness of flower border management, and promote the development of urban greening and landscaping.

[0066] Furthermore, if Figure 2 As shown, a comprehensive evaluation calculation of the long-term effect of a target flower border is performed according to the long-term effect association network to obtain a long-term effect comprehensive evaluation index of the target flower border, and the method includes:

[0067] Based on the multiple indicator influencing factors, the original plant survival rate index, the original plant area ratio index, the plant level ratio index, the theme presentation index, and the hard material location index are normalized to generate a comprehensive evaluation numerical index.

[0068] According to the multiple weight coefficients, the long-term impact association network is traversed to match the comprehensive evaluation numerical index to determine the matching evaluation target parameters.

[0069] The matching evaluation target parameters are evaluated based on the comprehensive evaluation numerical index to generate a plurality of long-term comprehensive scores for the target flower border.

[0070] The multiple long-term comprehensive scores are verified alternately, and weighted summation is performed according to the verification results to determine the long-term comprehensive evaluation index.

[0071] Exemplarily, each long-term evaluation index is normalized based on the multiple index influencing factors. Normalization is an important preprocessing step, which aims to convert indicators of different dimensions into a unified format that can be compared and integrated. For example, the original plant survival rate index, the original plant area ratio index, the plant level ratio index, the theme presentation index, and the hard material position index are all converted into values ​​between 0 and 1, where 1 represents the best state and 0 represents the worst state. These normalized indicators are called comprehensive evaluation numerical indicators, which provide a clear quantitative basis for subsequent analysis. Then, according to the preset multiple weight coefficients, the long-term impact association network is traversed. This network combines all evaluation indicators and weights to assign corresponding importance to each indicator. In this process, the comprehensive evaluation numerical indicator is matched with the evaluation target parameter in the long-term impact association network. The matching process ensures that the weight of each indicator is correctly applied to the corresponding evaluation data. Then, based on the comprehensive evaluation numerical indicator, the matched evaluation target parameters are evaluated to generate multiple long-term comprehensive scores of the target flower border. Each comprehensive score is calculated based on the evaluation result of a specific indicator and the weight of the indicator. For example, if the weight of plant survival rate is high, the score of this indicator will account for a larger proportion in the total evaluation. Finally, all generated long-term comprehensive scores are alternately verified. This step verifies the stability and reliability of each score to ensure that the evaluation results will not be erroneous due to the anomaly of a single data point. After alternating verification, a weighted sum is taken based on the verification results to finally determine the long-term comprehensive evaluation index, which is obtained by weighted averaging all verified scores according to their respective weights. It provides a quantitative measure that reflects the overall long-term performance of the target flower border.

[0072] Through the evaluation and calculation process, we can not only accurately assess the long-term effectiveness of flower borders, but also provide a scientific basis for the maintenance and improvement of flower borders, helping managers to develop more effective maintenance strategies to maintain the long-term beauty and functionality of flower borders. This significantly improves the accuracy and practicality of flower border evaluation, which is of great significance to urban greening and gardening management.

[0073] Furthermore, the matching evaluation target parameters are evaluated based on the comprehensive evaluation numerical index to generate a plurality of long-term comprehensive scores of the target flower border, and the method includes:

[0074] Taking the comprehensive evaluation numerical index as the evaluation reference value, the membership function method is used in combination with the evaluation reference value to calculate the original plant survival rate index, the original plant area proportion index, the plant level ratio index, the theme presentation index, and the hard material location index to obtain multiple membership function values. The multiple membership function values ​​have a one-to-one correspondence with the original plant survival rate index, the original plant area proportion index, the plant level ratio index, the theme presentation index, and the hard material location index.

[0075] According to the matching evaluation target parameters, the multiple membership function values ​​are added and averaged to obtain the multiple long-term comprehensive scores of the target flower border.

[0076] Specifically, the comprehensive evaluation numerical index is used as an evaluation reference value. These numerical indexes are the results obtained after normalization of each evaluation index, expressed as a value between 0 and 1, where 1 represents the best performance and 0 represents the worst performance. The comprehensive evaluation numerical index is used as an evaluation reference value to calculate the membership function value, because the membership function method can effectively convert the actual performance of the evaluation index into a standardized evaluation score. Next, the original plant survival rate index, the original plant area ratio index, the plant level ratio index, the theme presentation index, and the hard material position index are calculated using the membership function method in combination with the evaluation reference value. The membership function method is a method commonly used in fuzzy logic processing, which generates a membership value based on the relative position of the actual performance of each indicator relative to the ideal performance. For example, if the actual plant survival rate of a flower border is 80% and the ideal survival rate is set to 100%, the membership function may output an appropriate membership value based on this gap. Specifically, the calculation formula of the membership function value of each indicator is: X(μ)=X-Xmin / Xmax-Xmin, where X is the calculated value or score value of a certain indicator of a certain flower border, Xmax is the maximum value of the indicator, and Xmin is the minimum value of the indicator. The membership function values ​​of all indicators of a certain flower border are accumulated and the average value is obtained, which is the long-term comprehensive evaluation index of the flower border. The larger the value, the better the long-term effect. In other words, there is a one-to-one correspondence between each membership function value and the corresponding evaluation indicator, which means that each indicator has a specific membership function value to describe the relative merits of its performance. These membership function values ​​reflect the relative importance and performance quality of each indicator in the comprehensive evaluation of the target flower border in calculation. Finally, all membership function values ​​are summed and averaged according to the matching evaluation target parameters. This step involves combining all the calculated membership function values ​​to form a long-term comprehensive score representing the overall performance of the flower border. The method of summing and averaging ensures that the contribution of each indicator to the comprehensive score according to its importance and performance quality is properly reflected. Through the above method, multiple long-term comprehensive scores of the target flower border can be obtained, and each score is based on the precise calculation of the difference between the comprehensive actual performance and the ideal state. These scores are finally summarized into a comprehensive evaluation index reflecting the overall long-term effectiveness of the flower border, providing managers with a quantitative tool to evaluate and improve the design and maintenance strategies of the flower border. This systematic and scientific evaluation method provides solid decision-making support for garden management and helps to improve the sustainability and aesthetics of the flower border.

[0077] Through the technical solutions of the above embodiments, the long-term effectiveness evaluation method of flower borders combined with multi-dimensional indicators provided by the present application solves the technical problems that the existing long-term effectiveness evaluation of flower borders lacks systematic and quantitative flower border evaluation standards and is difficult to comprehensively measure their long-term ecological and aesthetic benefits. It achieves an accurate evaluation of the long-term effectiveness of flower borders, provides a scientific basis for optimizing maintenance and management, and improves the technical effect of the sustainability of their ecological and ornamental value.

[0078] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0079] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application.

Claims

1. A method for evaluating the long-term effectiveness of flower borders that combines multi-dimensional indicators, characterized in that: The method comprises: Extract the target flower border, perform low-maintenance management on the target flower border based on the evaluation cycle, and obtain a flower border maintenance record log; Performing a long-term determination analysis on the target flower border according to the flower border maintenance record log, and generating a determination result; Based on the determination result and the evaluation period, a multi-dimensional analysis is performed on the target flower border, and a plurality of flower border long-term effectiveness indicators are set according to the analysis result; Performing a multi-dimensional evaluation on the target flower border according to the plurality of flower border long-term effectiveness indicators to generate a multi-dimensional evaluation data set; A comprehensive evaluation calculation of the long-term effectiveness of the target flower border is performed according to the multidimensional evaluation data set to obtain a long-term comprehensive evaluation index of the target flower border, and an intelligent long-term effectiveness evaluation of the target flower border is performed according to the long-term comprehensive evaluation index.

2. The method for evaluating the long-term effectiveness of a flower border by combining multi-dimensional indicators as claimed in claim 1, characterized in that: Based on the determination result and the evaluation period, a multi-dimensional analysis is performed on the target flower border, and a plurality of flower border long-term effectiveness indicators are set according to the analysis result. The method includes: According to the evaluation cycle, the determination result is used as index data, and the flower border maintenance record log is traversed for hierarchical analysis to determine multiple dimensional layers, wherein the multiple dimensional layers include a plant survival analysis layer, a plant area analysis layer, a plant hierarchy analysis layer, a plant theme analysis layer, and a material maintenance analysis layer; Based on the evaluation cycle, real-time data extraction is performed on the target flower border according to the multiple dimensional layers to obtain multiple actual data of the target flower border; The multiple actual data are synchronized to the multiple dimensional layers in sequence, data analysis and comparison are performed on the multiple actual data through the multiple dimensional layers, and the multiple flower border long-term effectiveness indicators are set.

3. The method for evaluating the long-term effectiveness of a flower border by combining multi-dimensional indicators as claimed in claim 2, characterized in that: The multiple actual data are synchronized to the multiple dimensional layers in sequence, data parsing and comparing the multiple actual data are performed through the multiple dimensional layers, and the multiple flower border long-term effectiveness indicators are set, the method comprising: Analyze and classify the multiple actual data based on the multiple dimensional layers to obtain the actual number of surviving plants, actual plant coverage area parameters, actual plant layer distribution parameters, actual theme presentation effect parameters, and actual material maintenance position parameters; Comparing and calculating the initial number of plants with the number of surviving plants through the plant survival analysis layer, and setting the original plant survival rate index; By comparing and analyzing the initial plant coverage area parameter and the actual plant coverage area parameter through the plant area analysis layer, an original plant area ratio index is set; By comparing and analyzing the initial plant level distribution parameters with the actual plant level distribution parameters through the plant level analysis layer, a plant level ratio index is set; By using the plant theme analysis layer, the initial flower border theme and the actual theme presentation effect parameters are compared and analyzed to set a theme presentation index; Comparing and analyzing the initial hard material position and the actual material maintenance position parameters through the material maintenance analysis layer, and setting the hard material position index; The original plant survival rate index, the original plant area ratio index, the plant level ratio index, the theme presentation index, and the hard material position index are correlated and integrated to set the multiple flower border long-term indicators.

4. The method for evaluating the long-term effectiveness of a flower border by combining multi-dimensional indicators as claimed in claim 3, characterized in that: Performing a multi-dimensional evaluation on the target flower border according to the plurality of flower border long-term effectiveness indicators to generate a multi-dimensional evaluation data set, the method comprising: Performing plant survival assessment on the target flower border according to the original plant survival rate index to obtain survival assessment data; Evaluate the growth area of ​​the target flower border according to the original plant area ratio index to obtain area coverage evaluation data; Conducting construction level evaluation on the target flower border according to the plant level ratio index to obtain level ratio evaluation data; Evaluate the presentation degree of the target flower border according to the theme presentation degree index to obtain presentation effect evaluation data; Conducting material evaluation on the target flower border according to the hard material location index to obtain material maintenance evaluation data; The survival assessment data, the area coverage assessment data, the level ratio assessment data, the presentation effect assessment data, and the material maintenance assessment data are integrated through the multiple dimensional layers to obtain the multidimensional assessment data set.

5. The method for evaluating the long-term effectiveness of a flower border by combining multi-dimensional indicators as claimed in claim 4, characterized in that: The survival assessment data, the area coverage assessment data, the level ratio assessment data, the presentation effect assessment data, and the material maintenance assessment data are integrated through the multiple dimensional layers to obtain the multidimensional assessment data set, the method comprising: The target flower border is calculated according to the survival evaluation data through the plant survival analysis layer to generate a plant adaptation coefficient; Calculating the target flower border according to the area coverage assessment data through the plant area analysis layer to generate a plant growth coefficient; Calculating the target flower border according to the hierarchical proportion evaluation data through the plant hierarchical analysis layer to generate a plant construction proportion coefficient; The target flower border is calculated according to the presentation effect evaluation data by the plant theme analysis layer to generate a theme consistency coefficient; The material maintenance analysis layer calculates the target flower border according to the material maintenance evaluation data to generate a hard material utilization coefficient; The survival assessment data, the area coverage assessment data, the level proportion assessment data, the presentation effect assessment data and the material maintenance assessment data are sequentially subjected to multidimensional clustering integration according to the plant adaptation coefficient, the plant growth coefficient, the plant construction proportion coefficient, the theme consistency coefficient and the hard material usage coefficient to determine the multidimensional assessment data set.

6. The method for evaluating the long-term effectiveness of a flower border by combining multi-dimensional indicators as claimed in claim 5, characterized in that: Before performing a comprehensive evaluation calculation of the long-term effectiveness of the target flower border according to the multidimensional evaluation data set, the method includes: Based on the plant adaptation coefficient, the plant growth coefficient, the plant construction ratio coefficient, the theme consistency coefficient, and the hard material use coefficient, the survival assessment data, the area coverage assessment data, the level ratio assessment data, the presentation effect assessment data, and the material maintenance assessment data are subjected to impact analysis to obtain a plurality of indicator impact factors; According to the multiple indicator influencing factors, the original plant survival rate indicator, the original plant area ratio indicator, the plant level ratio indicator, the theme presentation index, and the hard material location indicator are weighted to obtain multiple weight coefficients; Conducting an impact association analysis based on the multiple indicator impact factors combined with the multiple weight coefficients to construct a long-term impact association network for the target flower border; A comprehensive evaluation calculation of the long-term effect of the target flower border is performed according to the long-term effect association network to obtain a comprehensive evaluation index of the long-term effect of the target flower border.

7. The method for evaluating the long-term effectiveness of a flower border by combining multi-dimensional indicators as claimed in claim 6, characterized in that: The method of calculating the long-term comprehensive evaluation of the target flower border according to the long-term impact association network to obtain the long-term comprehensive evaluation index of the target flower border includes: Based on the multiple index influencing factors, the original plant survival rate index, the original plant area ratio index, the plant level ratio index, the theme presentation index, and the hard material location index are normalized to generate a comprehensive evaluation numerical index; According to the multiple weight coefficients, traverse the long-term impact association network and match it with the comprehensive evaluation numerical index to determine the matching evaluation target parameters; Evaluate the matching evaluation target parameters based on the comprehensive evaluation numerical index to generate multiple long-term comprehensive scores for the target flower border; The multiple long-term comprehensive scores are verified alternately, and weighted summation is performed according to the verification results to determine the long-term comprehensive evaluation index.

8. The method for evaluating the long-term effectiveness of a flower border by combining multi-dimensional indicators as claimed in claim 7, characterized in that: The matching evaluation target parameters are evaluated based on the comprehensive evaluation numerical index to generate multiple long-term comprehensive scores of the target flower border, the method comprising: The comprehensive evaluation numerical index is used as an evaluation reference value, and the original plant survival rate index, the original plant area ratio index, the plant level ratio index, the theme presentation index, and the hard material location index are calculated by using a membership function method in combination with the evaluation reference value to obtain a plurality of membership function values, wherein the plurality of membership function values ​​have a one-to-one correspondence with the original plant survival rate index, the original plant area ratio index, the plant level ratio index, the theme presentation index, and the hard material location index; According to the matching evaluation target parameters, the multiple membership function values ​​are added and averaged to obtain the multiple long-term comprehensive scores of the target flower border.

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